OptiScale : a data-driven framework for selecting spatial and temporal resolution in biodiversity analysis

Autores: Martinez, Emilce Soledad; Tejada-Gutiérrez, Eva; Calcaterra, Martín; Eleiwa, Abderrahmane; Mateo-Fornes, Jordi; Solsona, Francesc; Sorribas, Albert; Marin-Sanguino, Alberto; Asín, Jesús; Vilaprinyo, Ester; Defacio, Raquel Alicia; Alves, Rui
Año de publicación: 2026
Idioma: inglés
Tipo de recurso: artículo
Estado: versión publicada
Descripción: Biodiversity analyses based on species-occurrence data are highly sensitive to the spatial and temporal resolutions at which observations are aggregated, leading to scale-dependent biases associated with the Modifiable Areal and Temporal Unit Problems (MAUP/MTUP). Yet resolution choices are often arbitrary, potentially introducing artefactual patterns and limiting the reproducibility and comparability of ecological studies. We present OptiScale, a data-driven framework that reduces biases arising from spatial and temporal aggregation before biodiversity analyses are performed. OptiScale integrates the species–area relationship theory and piecewise regression analysis to identify candidate spatial resolutions, and process control approaches to assess temporal stability. A multi-criteria selection procedure then identifies the optimal spatial–temporal resolution (OpR) that minimizes aggregation effects on data structure while balancing data aggregation, sparsity, variability, and model behavior. OptiScale is not designed to identify universal ecological scales or infer optimal resolutions for specific ecological processes. Rather, it provides a reproducible and statistically grounded approach for selecting analysis resolutions in heterogeneous occurrence datasets. Evaluation across three case studies spanning contrasting ecological and sampling contexts showed that OpR is inherently context dependent, emerging from trade-offs among spatial aggregation, temporal stability, and data sparsity. By replacing arbitrary resolution choices with an explicit data-driven procedure, OptiScale reduces aggregation bias and improves the robustness and comparability of biodiversity analyses. The framework is scalable, adaptable to diverse datasets, and transferable beyond plant biodiversity to any georeferenced occurrence data.
EEA Pergamino
Fil: Martinez, Emilce. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino. Laboratorio de Semillas. Banco Activo de Germoplasma; Argentina
Fil: Martinez, Emilce. Universidad de Lleida. Facultad de Medicina. Departamento de Ciencias Médicas Básicas. Grupo de Biología de Sistemas; España
Fil: Tejada-Gutiérrez, Eva. Universidad de Lleida. Facultad de Medicina. Departamento de Ciencias Médicas Básicas. Grupo de Biología de Sistemas; España
Fil: Calcaterra, Martín. Universidad Nacional del Noroeste de la Provincia de Buenos Aires. Escuela de Ciencias Agrarias, Naturales y ambientales; Argentina
Fil: Eleiwa, Abderrahmane. Universidad de Lleida. Facultad de Medicina. Departamento de Ciencias Médicas Básicas. Grupo de Biología de Sistemas; España
Fil: Mateo-Fornes, Jordi. Universidad de Lleida. Departamento de Ingeniería Informática y Diseño Digital; España
Fil: Solsona, Francesc. Universidad de Lleida. Departamento de Ingeniería Informática y Diseño Digital; España
Fil: Sorribas, Albert. Universidad de Lleida. Facultad de Medicina. Departamento de Ciencias Médicas Básicas. Grupo de Biología de Sistemas; España
Fil: Marin-Sanguino, Alberto. Universidad de Lleida. Facultad de Medicina. Departamento de Ciencias Médicas Básicas. Grupo de Biología de Sistemas; España
Fil: Asín, Jesús. Universidad de Zaragoza. Departamento de Métodos Estadísticos; España
Fil: Vilaprinyo, Ester. Universidad de Lleida. Facultad de Medicina. Departamento de Ciencias Médicas Básicas. Grupo de Biología de Sistemas; España
Fil: Defacio, Raquel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino. Recursos Genéticos; Argentina
Fil: Alves, Rui. Universidad de Lleida. Facultad de Medicina. Departamento de Ciencias Médicas Básicas. Grupo de Biología de Sistemas; España
Fuente: Ecological Informatics 99 : 104084. (November 2026)
Materia: Biodiversidad
Ecología
Biodiversity
Ecology
Remote Sensing
Teledetección
Spatial-temporal Aggregation
Scale Optimization
Ecological Modeling
Mathematical Ecology
Nivel de accesibilidad: acceso abierto
Condiciones de uso: http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
Institución: Instituto Nacional de Tecnología Agropecuaria
OAI Identificador: oai:localhost:20.500.12123/28131

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spelling	OptiScale : a data-driven framework for selecting spatial and temporal resolution in biodiversity analysisMartinez, Emilce SoledadTejada-Gutiérrez, EvaCalcaterra, MartínEleiwa, AbderrahmaneMateo-Fornes, JordiSolsona, FrancescSorribas, AlbertMarin-Sanguino, AlbertoAsín, JesúsVilaprinyo, EsterDefacio, Raquel AliciaAlves, RuiBiodiversidadEcologíaBiodiversityEcologyRemote SensingTeledetecciónSpatial-temporal AggregationScale OptimizationEcological ModelingMathematical EcologyBiodiversity analyses based on species-occurrence data are highly sensitive to the spatial and temporal resolutions at which observations are aggregated, leading to scale-dependent biases associated with the Modifiable Areal and Temporal Unit Problems (MAUP/MTUP). Yet resolution choices are often arbitrary, potentially introducing artefactual patterns and limiting the reproducibility and comparability of ecological studies. We present OptiScale, a data-driven framework that reduces biases arising from spatial and temporal aggregation before biodiversity analyses are performed. OptiScale integrates the species–area relationship theory and piecewise regression analysis to identify candidate spatial resolutions, and process control approaches to assess temporal stability. A multi-criteria selection procedure then identifies the optimal spatial–temporal resolution (OpR) that minimizes aggregation effects on data structure while balancing data aggregation, sparsity, variability, and model behavior. OptiScale is not designed to identify universal ecological scales or infer optimal resolutions for specific ecological processes. Rather, it provides a reproducible and statistically grounded approach for selecting analysis resolutions in heterogeneous occurrence datasets. Evaluation across three case studies spanning contrasting ecological and sampling contexts showed that OpR is inherently context dependent, emerging from trade-offs among spatial aggregation, temporal stability, and data sparsity. By replacing arbitrary resolution choices with an explicit data-driven procedure, OptiScale reduces aggregation bias and improves the robustness and comparability of biodiversity analyses. The framework is scalable, adaptable to diverse datasets, and transferable beyond plant biodiversity to any georeferenced occurrence data.EEA PergaminoFil: Martinez, Emilce. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino. Laboratorio de Semillas. Banco Activo de Germoplasma; ArgentinaFil: Martinez, Emilce. Universidad de Lleida. Facultad de Medicina. Departamento de Ciencias Médicas Básicas. Grupo de Biología de Sistemas; EspañaFil: Tejada-Gutiérrez, Eva. Universidad de Lleida. Facultad de Medicina. Departamento de Ciencias Médicas Básicas. Grupo de Biología de Sistemas; EspañaFil: Calcaterra, Martín. Universidad Nacional del Noroeste de la Provincia de Buenos Aires. Escuela de Ciencias Agrarias, Naturales y ambientales; ArgentinaFil: Eleiwa, Abderrahmane. Universidad de Lleida. Facultad de Medicina. Departamento de Ciencias Médicas Básicas. Grupo de Biología de Sistemas; EspañaFil: Mateo-Fornes, Jordi. Universidad de Lleida. Departamento de Ingeniería Informática y Diseño Digital; EspañaFil: Solsona, Francesc. Universidad de Lleida. Departamento de Ingeniería Informática y Diseño Digital; EspañaFil: Sorribas, Albert. Universidad de Lleida. Facultad de Medicina. Departamento de Ciencias Médicas Básicas. Grupo de Biología de Sistemas; EspañaFil: Marin-Sanguino, Alberto. Universidad de Lleida. Facultad de Medicina. Departamento de Ciencias Médicas Básicas. Grupo de Biología de Sistemas; EspañaFil: Asín, Jesús. Universidad de Zaragoza. Departamento de Métodos Estadísticos; EspañaFil: Vilaprinyo, Ester. Universidad de Lleida. Facultad de Medicina. Departamento de Ciencias Médicas Básicas. Grupo de Biología de Sistemas; EspañaFil: Defacio, Raquel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino. Recursos Genéticos; ArgentinaFil: Alves, Rui. Universidad de Lleida. Facultad de Medicina. Departamento de Ciencias Médicas Básicas. Grupo de Biología de Sistemas; EspañaElsevier2026-10-15T14:55:47Z2026-10-15T14:55:47Z2026-11info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/28131https://www.sciencedirect.com/science/article/pii/S15749541260049291574-95411878-0512https://doi.org/10.1016/j.ecoinf.2026.104084Ecological Informatics 99 : 104084. (November 2026)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)2026-11-05T11:51:34Zoai:localhost:20.500.12123/28131instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2026-11-05 11:51:35.3INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv	OptiScale : a data-driven framework for selecting spatial and temporal resolution in biodiversity analysis
title	OptiScale : a data-driven framework for selecting spatial and temporal resolution in biodiversity analysis
spellingShingle	OptiScale : a data-driven framework for selecting spatial and temporal resolution in biodiversity analysis Martinez, Emilce Soledad Biodiversidad Ecología Biodiversity Ecology Remote Sensing Teledetección Spatial-temporal Aggregation Scale Optimization Ecological Modeling Mathematical Ecology
title_short	OptiScale : a data-driven framework for selecting spatial and temporal resolution in biodiversity analysis
title_full	OptiScale : a data-driven framework for selecting spatial and temporal resolution in biodiversity analysis
title_fullStr	OptiScale : a data-driven framework for selecting spatial and temporal resolution in biodiversity analysis
title_full_unstemmed	OptiScale : a data-driven framework for selecting spatial and temporal resolution in biodiversity analysis
title_sort	OptiScale : a data-driven framework for selecting spatial and temporal resolution in biodiversity analysis
dc.creator.none.fl_str_mv	Martinez, Emilce Soledad Tejada-Gutiérrez, Eva Calcaterra, Martín Eleiwa, Abderrahmane Mateo-Fornes, Jordi Solsona, Francesc Sorribas, Albert Marin-Sanguino, Alberto Asín, Jesús Vilaprinyo, Ester Defacio, Raquel Alicia Alves, Rui
author	Martinez, Emilce Soledad
author_facet	Martinez, Emilce Soledad Tejada-Gutiérrez, Eva Calcaterra, Martín Eleiwa, Abderrahmane Mateo-Fornes, Jordi Solsona, Francesc Sorribas, Albert Marin-Sanguino, Alberto Asín, Jesús Vilaprinyo, Ester Defacio, Raquel Alicia Alves, Rui
author_role	author
author2	Tejada-Gutiérrez, Eva Calcaterra, Martín Eleiwa, Abderrahmane Mateo-Fornes, Jordi Solsona, Francesc Sorribas, Albert Marin-Sanguino, Alberto Asín, Jesús Vilaprinyo, Ester Defacio, Raquel Alicia Alves, Rui
author2_role	author author author author author author author author author author author
dc.subject.none.fl_str_mv	Biodiversidad Ecología Biodiversity Ecology Remote Sensing Teledetección Spatial-temporal Aggregation Scale Optimization Ecological Modeling Mathematical Ecology
topic	Biodiversidad Ecología Biodiversity Ecology Remote Sensing Teledetección Spatial-temporal Aggregation Scale Optimization Ecological Modeling Mathematical Ecology
dc.description.none.fl_txt_mv	Biodiversity analyses based on species-occurrence data are highly sensitive to the spatial and temporal resolutions at which observations are aggregated, leading to scale-dependent biases associated with the Modifiable Areal and Temporal Unit Problems (MAUP/MTUP). Yet resolution choices are often arbitrary, potentially introducing artefactual patterns and limiting the reproducibility and comparability of ecological studies. We present OptiScale, a data-driven framework that reduces biases arising from spatial and temporal aggregation before biodiversity analyses are performed. OptiScale integrates the species–area relationship theory and piecewise regression analysis to identify candidate spatial resolutions, and process control approaches to assess temporal stability. A multi-criteria selection procedure then identifies the optimal spatial–temporal resolution (OpR) that minimizes aggregation effects on data structure while balancing data aggregation, sparsity, variability, and model behavior. OptiScale is not designed to identify universal ecological scales or infer optimal resolutions for specific ecological processes. Rather, it provides a reproducible and statistically grounded approach for selecting analysis resolutions in heterogeneous occurrence datasets. Evaluation across three case studies spanning contrasting ecological and sampling contexts showed that OpR is inherently context dependent, emerging from trade-offs among spatial aggregation, temporal stability, and data sparsity. By replacing arbitrary resolution choices with an explicit data-driven procedure, OptiScale reduces aggregation bias and improves the robustness and comparability of biodiversity analyses. The framework is scalable, adaptable to diverse datasets, and transferable beyond plant biodiversity to any georeferenced occurrence data. EEA Pergamino Fil: Martinez, Emilce. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino. Laboratorio de Semillas. Banco Activo de Germoplasma; Argentina Fil: Martinez, Emilce. Universidad de Lleida. Facultad de Medicina. Departamento de Ciencias Médicas Básicas. Grupo de Biología de Sistemas; España Fil: Tejada-Gutiérrez, Eva. Universidad de Lleida. Facultad de Medicina. Departamento de Ciencias Médicas Básicas. Grupo de Biología de Sistemas; España Fil: Calcaterra, Martín. Universidad Nacional del Noroeste de la Provincia de Buenos Aires. Escuela de Ciencias Agrarias, Naturales y ambientales; Argentina Fil: Eleiwa, Abderrahmane. Universidad de Lleida. Facultad de Medicina. Departamento de Ciencias Médicas Básicas. Grupo de Biología de Sistemas; España Fil: Mateo-Fornes, Jordi. Universidad de Lleida. Departamento de Ingeniería Informática y Diseño Digital; España Fil: Solsona, Francesc. Universidad de Lleida. Departamento de Ingeniería Informática y Diseño Digital; España Fil: Sorribas, Albert. Universidad de Lleida. Facultad de Medicina. Departamento de Ciencias Médicas Básicas. Grupo de Biología de Sistemas; España Fil: Marin-Sanguino, Alberto. Universidad de Lleida. Facultad de Medicina. Departamento de Ciencias Médicas Básicas. Grupo de Biología de Sistemas; España Fil: Asín, Jesús. Universidad de Zaragoza. Departamento de Métodos Estadísticos; España Fil: Vilaprinyo, Ester. Universidad de Lleida. Facultad de Medicina. Departamento de Ciencias Médicas Básicas. Grupo de Biología de Sistemas; España Fil: Defacio, Raquel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino. Recursos Genéticos; Argentina Fil: Alves, Rui. Universidad de Lleida. Facultad de Medicina. Departamento de Ciencias Médicas Básicas. Grupo de Biología de Sistemas; España
description	Biodiversity analyses based on species-occurrence data are highly sensitive to the spatial and temporal resolutions at which observations are aggregated, leading to scale-dependent biases associated with the Modifiable Areal and Temporal Unit Problems (MAUP/MTUP). Yet resolution choices are often arbitrary, potentially introducing artefactual patterns and limiting the reproducibility and comparability of ecological studies. We present OptiScale, a data-driven framework that reduces biases arising from spatial and temporal aggregation before biodiversity analyses are performed. OptiScale integrates the species–area relationship theory and piecewise regression analysis to identify candidate spatial resolutions, and process control approaches to assess temporal stability. A multi-criteria selection procedure then identifies the optimal spatial–temporal resolution (OpR) that minimizes aggregation effects on data structure while balancing data aggregation, sparsity, variability, and model behavior. OptiScale is not designed to identify universal ecological scales or infer optimal resolutions for specific ecological processes. Rather, it provides a reproducible and statistically grounded approach for selecting analysis resolutions in heterogeneous occurrence datasets. Evaluation across three case studies spanning contrasting ecological and sampling contexts showed that OpR is inherently context dependent, emerging from trade-offs among spatial aggregation, temporal stability, and data sparsity. By replacing arbitrary resolution choices with an explicit data-driven procedure, OptiScale reduces aggregation bias and improves the robustness and comparability of biodiversity analyses. The framework is scalable, adaptable to diverse datasets, and transferable beyond plant biodiversity to any georeferenced occurrence data.
publishDate	2026
dc.date.none.fl_str_mv	2026-10-15T14:55:47Z 2026-10-15T14:55:47Z 2026-11
dc.type.none.fl_str_mv	info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo
format	article
status_str	publishedVersion
dc.identifier.none.fl_str_mv	http://hdl.handle.net/20.500.12123/28131 https://www.sciencedirect.com/science/article/pii/S1574954126004929 1574-9541 1878-0512 https://doi.org/10.1016/j.ecoinf.2026.104084
url	http://hdl.handle.net/20.500.12123/28131 https://www.sciencedirect.com/science/article/pii/S1574954126004929 https://doi.org/10.1016/j.ecoinf.2026.104084
identifier_str_mv	1574-9541 1878-0512
dc.language.none.fl_str_mv	eng
language	eng
dc.rights.none.fl_str_mv	info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
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rights_invalid_str_mv	http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv	application/pdf
dc.publisher.none.fl_str_mv	Elsevier
publisher.none.fl_str_mv	Elsevier
dc.source.none.fl_str_mv	Ecological Informatics 99 : 104084. (November 2026) reponame:INTA Digital (INTA) instname:Instituto Nacional de Tecnología Agropecuaria
reponame_str	INTA Digital (INTA)
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OptiScale : a data-driven framework for selecting spatial and temporal resolution in biodiversity analysis

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